Person Detection and Tracking using Binocular Lucas-Kanade Feature Tracking and K-means Clustering

1. Person Detection and Tracking using Binocular Lucas-Kanade Feature Tracking and K-means Clustering Chris Dunkel Committee: Dr. Stanley Birchfield, Committee Chair Dr. Adam Hoover Dr. Richard Brooks

2. The Importance of Person Detection • Critical technology for machine/human interaction • Basis for future research into machine/human interaction • Many applications: • Person avoidance by robots in a factory • Following of people with heavy equipment or tools • Autonomous patrolling of secure areas

3. Other Approaches • Color Based [1,2,3,4] • Simple, fast • Can be confused by similar-color environments • Optical Flow/Motion Based[5,6] • Robust to color or lighting changes • Person must move relative to background • Dense Stereo Matching[7] • Constructs accurate 3D models of the environment • Slow, may have difficulty with people near the background • Pattern Based[8] • Low false positive rate. Works well on low-resolution images in adverse conditions • Slow (4 fps). Requires stationary camera.

4. Our Approach • Inspired by the work of Chen and Birchfield [12] • Stereo based Lucas-Kanade [9, 10] • Detect and track feature points • Calculate sparse disparity map • Segment scene using k-means Clustering • Detect faces using Viola-Jones detector [11] • Detect Person using results of k-means and Viola-Jones • Track Person using modified detection procedure

5. Person Detection

6. (0,0) d y d x Lucas-Kanade • Originally intended for fast image registration • Selects features based on texture: • Coefficient matrix based on covariance of image gradients within a window around the proposed feature • Eigenvalues of coefficient matrix must be large and similarly valued • Tracks features based on error: • Error between image intensities • L2 Norm (Sum of Squares) used to define error • Small changes between frames assumed

7. Sparse Disparity Map Generation • Track points from left frame to right frame • Track points back from right frame to left frame to check disparity • Keep point if εd is less than user-defined threshold

8. Tracking Velocity • Change in feature location in (x,y,d) recorded from frame to frame, giving (Δx,Δy,Δd) • Each feature located in R6 space: (x,y,d,Δx,Δy,Δd) • Idea is to segment for motion, as well as position • Δvalues provide an extra layer of classification, but not enough to stand on their own

10. Clustering Methods • All methods iterative • K-means • Simple, effective • Assigns features to clusters based on distance to cluster means • Fuzzy C-means • More complex • Features weighted for each cluster based on distance to cluster means • Expectation Maximization (EM) • General clustering algorithm • Can be used for many applications • Cluster membership based on probability density function defined by user

11. K-means • Select initial cluster means • Assign points to clusters based on distance to means • Recalculate means using new cluster membership • Repeat steps 2 and 3 until clusters are stable 1 2 3 4

12. Mahalanobis Distance • Similar to Euclidean distance • Weights each dimension by variance, σ2 where (0,0) (0,0) y d x x

14. Viola-Jones Face Detection • General method for fast object detection • Integral images used for fast feature evaluation • Features reminiscent of Haar basis functions • AdaBoost used to select best classifier Haar-based Classifiers Overlaid on Sample Faces • Use cascade structure to combine successively complex classifiers • Simple classifier used to eliminate large regions of the image from consideration • Increasingly complex classifiers used to eliminate remaining features • Features judged based on image intensity

16. Detecting the Person • Create bounding box based on face • Merge results from face detector and clustering • Remove points that qualify as outliers • Enter Tracking if person found

17. Person Tracking

18. Updating the Face • Updates face location when not running Viola-Jones • Update position based on movement in tracked person from t-1 to t • Update size based on change in person disparity

19. Number of feature points in person cluster saved at the start of tracking Number of good feature points monitored throughout tracking Person “lost” if number of points tracked drops below a user-defined percentage of the original number (25% in our case) A person in danger of being lost Losing the Person

20. System Overview • Computer Hardware: • Dell Inspiron 700m laptop • 1.6 GHz Intel Centrino processor • Computer Software: • Windows XP Service Pack 2 • Microsoft Visual C++ 6.0 • Blepo Computer Vision Library • Cameras: • ImageSource DFK 21F04 CCD • Daisy-chain Firewire (IEEE 1394) computer interface • 320 x 240 Resolution

21. System Overview (Cont.) • Mobile Robot: • ActivMedia Pioneer P3-DX • Interface through serial (RS-232) using ActivMedia Robotics Interface for Application (ARIA) API • Robot Control: • Proportional Controller used to drive robot • Based on x-position and disparity of the person • Cf = 50 Cr = 0.75

22. Experimental Results Frame 110: Person Found Frame 410: Face Detector Error Frame 400: Person Tracked against Background Frame 670: Face Error Corrected

23. Experimental Results (cont.) Frame 380: Person Detect (Face Error) Frame 630: Person Tracked Across Image Frame 450: Face Corrected Frame 800: Person Tracked During Partial Occlusion

24. Experimental Results (cont.) Tracking in a Low Contrast Environment Tracking during Self-Occlusion and Loss of Face

25. Videos

26. Algorithm Comparison • Color Based • Works in low-contrast environments • Robust to lighting changes • Optical Flow/Motion Based • Person does not need to move relative to camera • Dense Stereo Matching • Real time • Pattern Based • Real time • Does not require stationary camera

27. Conclusions & Future Work • Advantages: • Does not need color • Can detect person with no relative motion • Robust to partial and self occlusion • Future Work: • Less reliance on face, and addition of person qualifiers such as motion estimation • Testing to determine optimal thresholds • Application of other clustering methods • Person classification / recognition

28. References [1] Sidenbladh et al., A Person Following Behavior for a Mobile Robot, 1999 [2] Tarokh and Ferrari, Robotic Person Following Using Fuzzy Control for Image Segmentation, 2003 [3] Kwon et al., Person Tracking with a Mobile Robot Using Two Uncalibrated Independently Moving Cameras, 2005 [4] Schlegel et al., Vision Based Person Tracking with a Mobile Robot, 1998 [5] Paggio et al., An Optical-Flow Person Following Behaviour, 1998 [6] Chivilo et al., Follow-the-leader Behavior through Optical Flow Minimization, 2004 [7] Beymer and Konolige, Tracking People from a Mobile Platform, 2001 [8] Viola et al., Detecting Pedestrians Using Patterns of Motion and Appearance, 2003 [9] Lucas and Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, 1981 [10] Tomasi and Kanade, Detection and Tracking of Point Features, 1991 [11] Viola and Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, 2001 [12] Chen and Birchfield, Person Following with a Mobile Robot Using Binocular Feature-Based Tracking, 2007

29. Questions?